58,000 = 2% of PR population!
Mathematical: make prediction based on differential equations designed to describe how nature works
Statistical: ajust models to observed data. We tend to limit to few models that have worked well in other scenarios.
Visualization: less assumptions, but permits us to see patterns.
“It’s hard to make predictions especially about the future” - Yogi Berra
“Forecasting s-curves is hard” - Constance Crozier

Source: Puerto Rico Institute of Statistics
Tries to estimate prevalence
If we only perform tests on symptomatic this estimate will be biased
If we do more universal tests, the estimate is less biased.
But cases will grow with the number of tests regardless of prevalence.
We did not know how many tests were being performed.
https://www.mass.gov/info-details/covid-19-response-reporting#covid-19-daily-dashboard-
Positivity rate is their main indicator
They also look at hospitalizations and deaths
Deaths in March 2019: 2489
Deaths in March 2020: 2720
Estimating excess deaths in PR is complicated because - There is natural variability (not just PR) - Population change changes - Demographics change
Another problem is incomplete data
We can’t just compare two years
But this could change rapidly
And will we know soon enough?
Once we have data in a nice form, showing data is easy
hosp <- read_xlsx("data/Dash Total.xlsx")
hosp %>%
ggplot(aes(Fecha, `Total de Personas Hospitalizadas COVID`)) +
geom_point() + geom_smooth(span = 0.3)